return thisThread->state;
}
- // Skill structure is used to implement strength limit
+ // Skill structure is used to implement strength limit. If we have an uci_elo then
+ // we convert it to a suitable fractional skill level using anchoring to CCRL Elo
+ // (goldfish 1.13 = 2000) and a fit through Ordo derived Elo for match (TC 60+0.6)
+ // results spanning a wide range of k values.
struct Skill {
- explicit Skill(int l) : level(l) {}
- bool enabled() const { return level < 20; }
- bool time_to_pick(Depth depth) const { return depth == 1 + level; }
+ Skill(int skill_level, int uci_elo) {
+ if (uci_elo)
+ level = std::clamp(std::pow((uci_elo - 1346.6) / 143.4, 1 / 0.806), 0.0, 20.0);
+ else
+ level = double(skill_level);
+ }
+ bool enabled() const { return level < 20.0; }
+ bool time_to_pick(Depth depth) const { return depth == 1 + int(level); }
Move pick_best(size_t multiPV);
- int level;
+ double level;
Move best = MOVE_NONE;
};
Time.availableNodes += Limits.inc[us] - Threads.nodes_searched();
Thread* bestThread = this;
+ Skill skill = Skill(Options["Skill Level"], Options["UCI_LimitStrength"] ? int(Options["UCI_Elo"]) : 0);
if ( int(Options["MultiPV"]) == 1
&& !Limits.depth
- && !(Skill(Options["Skill Level"]).enabled() || int(Options["UCI_LimitStrength"]))
+ && !skill.enabled()
&& rootMoves[0].pv[0] != MOVE_NONE)
bestThread = Threads.get_best_thread();
// The latter is needed for statScore and killer initialization.
Stack stack[MAX_PLY+10], *ss = stack+7;
Move pv[MAX_PLY+1];
- Value bestValue, alpha, beta, delta;
+ Value alpha, beta, delta;
Move lastBestMove = MOVE_NONE;
Depth lastBestMoveDepth = 0;
MainThread* mainThread = (this == Threads.main() ? Threads.main() : nullptr);
std::fill(&lowPlyHistory[MAX_LPH - 2][0], &lowPlyHistory.back().back() + 1, 0);
size_t multiPV = size_t(Options["MultiPV"]);
-
- // Pick integer skill levels, but non-deterministically round up or down
- // such that the average integer skill corresponds to the input floating point one.
- // UCI_Elo is converted to a suitable fractional skill level, using anchoring
- // to CCRL Elo (goldfish 1.13 = 2000) and a fit through Ordo derived Elo
- // for match (TC 60+0.6) results spanning a wide range of k values.
- PRNG rng(now());
- double floatLevel = Options["UCI_LimitStrength"] ?
- std::clamp(std::pow((Options["UCI_Elo"] - 1346.6) / 143.4, 1 / 0.806), 0.0, 20.0) :
- double(Options["Skill Level"]);
- int intLevel = int(floatLevel) +
- ((floatLevel - int(floatLevel)) * 1024 > rng.rand<unsigned>() % 1024 ? 1 : 0);
- Skill skill(intLevel);
+ Skill skill(Options["Skill Level"], Options["UCI_LimitStrength"] ? int(Options["UCI_Elo"]) : 0);
// When playing with strength handicap enable MultiPV search that we will
// use behind the scenes to retrieve a set of possible moves.
nodesLastExplosive = nodes;
nodesLastNormal = nodes;
- state = EXPLOSION_NONE;
- trend = SCORE_ZERO;
+ state = EXPLOSION_NONE;
+ trend = SCORE_ZERO;
+ optimism[ us] = Value(25);
+ optimism[~us] = -optimism[us];
int searchAgainCounter = 0;
// Reset aspiration window starting size
if (rootDepth >= 4)
{
- Value prev = rootMoves[pvIdx].previousScore;
+ Value prev = rootMoves[pvIdx].averageScore;
delta = Value(17) + int(prev) * prev / 16384;
alpha = std::max(prev - delta,-VALUE_INFINITE);
beta = std::min(prev + delta, VALUE_INFINITE);
- // Adjust trend based on root move's previousScore (dynamic contempt)
- int tr = 113 * prev / (abs(prev) + 147);
-
+ // Adjust trend and optimism based on root move's previousScore
+ int tr = sigmoid(prev, 0, 0, 147, 113, 1);
trend = (us == WHITE ? make_score(tr, tr / 2)
: -make_score(tr, tr / 2));
+
+ int opt = sigmoid(prev, 0, 25, 147, 14464, 256);
+ optimism[ us] = Value(opt);
+ optimism[~us] = -optimism[us];
}
// Start with a small aspiration window and, in the case of a fail
if (alpha >= beta)
return alpha;
}
+ else
+ thisThread->rootDelta = beta - alpha;
assert(0 <= ss->ply && ss->ply < MAX_PLY);
// Use static evaluation difference to improve quiet move ordering
if (is_ok((ss-1)->currentMove) && !(ss-1)->inCheck && !priorCapture)
{
- int bonus = std::clamp(-depth * 4 * int((ss-1)->staticEval + ss->staticEval), -1000, 1000);
+ int bonus = std::clamp(-16 * int((ss-1)->staticEval + ss->staticEval), -2000, 2000);
thisThread->mainHistory[~us][from_to((ss-1)->currentMove)] << bonus;
}
// Step 7. Futility pruning: child node (~50 Elo).
// The depth condition is important for mate finding.
- if ( !PvNode
+ if ( !ss->ttPv
&& depth < 9
&& eval - futility_margin(depth, improving) >= beta
- && eval < VALUE_KNOWN_WIN) // Do not return unproven wins
+ && eval < 15000) // 50% larger than VALUE_KNOWN_WIN, but smaller than TB wins.
return eval;
// Step 8. Null move search with verification search (~40 Elo)
}
else
{
+ int history = (*contHist[0])[movedPiece][to_sq(move)]
+ + (*contHist[1])[movedPiece][to_sq(move)]
+ + (*contHist[3])[movedPiece][to_sq(move)];
+
// Continuation history based pruning (~20 Elo)
- if (lmrDepth < 5
- && (*contHist[0])[movedPiece][to_sq(move)]
- + (*contHist[1])[movedPiece][to_sq(move)]
- + (*contHist[3])[movedPiece][to_sq(move)] < -3000 * depth + 3000)
+ if ( lmrDepth < 5
+ && history < -3000 * depth + 3000)
continue;
+ history += thisThread->mainHistory[us][from_to(move)];
+
// Futility pruning: parent node (~5 Elo)
if ( !ss->inCheck
&& lmrDepth < 8
- && ss->staticEval + 172 + 145 * lmrDepth <= alpha)
+ && ss->staticEval + 142 + 139 * lmrDepth + history / 64 <= alpha)
continue;
// Prune moves with negative SEE (~20 Elo)
{
Depth r = reduction(improving, depth, moveCount, rangeReduction > 2);
- // Decrease reduction if on the PV (~2 Elo)
+ // Decrease reduction at some PvNodes (~2 Elo)
if ( PvNode
- && bestMoveCount <= 3)
+ && bestMoveCount <= 3
+ && beta - alpha >= thisThread->rootDelta / 4)
r--;
// Decrease reduction if position is or has been on the PV
// In general we want to cap the LMR depth search at newDepth. But if reductions
// are really negative and movecount is low, we allow this move to be searched
// deeper than the first move (this may lead to hidden double extensions).
- int deeper = r >= -1 ? 0
- : moveCount <= 5 ? 2
- : PvNode && depth > 6 ? 1
- : 0;
+ int deeper = r >= -1 ? 0
+ : moveCount <= 5 ? 2
+ : PvNode && depth > 6 ? 1
+ : cutNode && moveCount <= 7 ? 1
+ : 0;
Depth d = std::clamp(newDepth - r, 1, newDepth + deeper);
RootMove& rm = *std::find(thisThread->rootMoves.begin(),
thisThread->rootMoves.end(), move);
+ rm.averageScore = rm.averageScore != -VALUE_INFINITE ? (2 * value + rm.averageScore) / 3 : value;
+
// PV move or new best move?
if (moveCount == 1 || value > alpha)
{
Key posKey;
Move ttMove, move, bestMove;
Depth ttDepth;
- Value bestValue, value, ttValue, futilityValue, futilityBase, oldAlpha;
+ Value bestValue, value, ttValue, futilityValue, futilityBase;
bool pvHit, givesCheck, captureOrPromotion;
int moveCount;
if (PvNode)
{
- oldAlpha = alpha; // To flag BOUND_EXACT when eval above alpha and no available moves
(ss+1)->pv = pv;
ss->pv[0] = MOVE_NONE;
}
// Save gathered info in transposition table
tte->save(posKey, value_to_tt(bestValue, ss->ply), pvHit,
- bestValue >= beta ? BOUND_LOWER :
- PvNode && bestValue > oldAlpha ? BOUND_EXACT : BOUND_UPPER,
+ bestValue >= beta ? BOUND_LOWER : BOUND_UPPER,
ttDepth, bestMove, ss->staticEval);
assert(bestValue > -VALUE_INFINITE && bestValue < VALUE_INFINITE);
thisThread->mainHistory[us][from_to(move)] << bonus;
update_continuation_histories(ss, pos.moved_piece(move), to_sq(move), bonus);
- // Penalty for reversed move in case of moved piece not being a pawn
- if (type_of(pos.moved_piece(move)) != PAWN)
- thisThread->mainHistory[us][from_to(reverse_move(move))] << -bonus;
-
// Update countermove history
if (is_ok((ss-1)->currentMove))
{
// RootMoves are already sorted by score in descending order
Value topScore = rootMoves[0].score;
int delta = std::min(topScore - rootMoves[multiPV - 1].score, PawnValueMg);
- int weakness = 120 - 2 * level;
int maxScore = -VALUE_INFINITE;
+ double weakness = 120 - 2 * level;
// Choose best move. For each move score we add two terms, both dependent on
// weakness. One is deterministic and bigger for weaker levels, and one is
for (size_t i = 0; i < multiPV; ++i)
{
// This is our magic formula
- int push = ( weakness * int(topScore - rootMoves[i].score)
- + delta * (rng.rand<unsigned>() % weakness)) / 128;
+ int push = int(( weakness * int(topScore - rootMoves[i].score)
+ + delta * (rng.rand<unsigned>() % int(weakness))) / 128);
if (rootMoves[i].score + push >= maxScore)
{
projects / stockfish / blobdiff